| Female from trk2022tr_r | GENDER | n |
|---|---|---|
| 0 | 1. Male | 8664 |
| 1 | 2. Female | 12245 |
| NA | NA | 3 |
HRS 2016 - Cognition classification
Data
The data used in this analysis comes from the 2016 wave of the HRS study. The data was directly downloaded from their website.
The files used are:
trk2022tr_r.dtaH16A_R.dtaH16D_R.dta
To norm the factor scores, RNJ provided a dataset that identified the participants with normal cognition in the HCAP sample.
Recode demographic variables
This section shows the recoding of the demographic variables.
Age came from the variable PA019 and no recoding was done.
Years of education came from the variable SCHLYRS and no recoding was done. Five participants had their values changed based on RNJ’s code.
Female came from the variable GENDER.
Black came from the variables RACE and HISPANIC.
| Black or African-American (not Hispanic) from trk2022tr_r | RACE | HISPANIC | n |
|---|---|---|---|
| 0 | 0. Not obtained | 0. Not obtained | 5 |
| 0 | 0. Not obtained | 1. Hispanic, Mexican | 40 |
| 0 | 0. Not obtained | 2. Hispanic, Other | 23 |
| 0 | 0. Not obtained | 3. Hispanic, type unknown | 1 |
| 0 | 0. Not obtained | 5. Non-Hispanic | 9 |
| 0 | 1. White/Caucasian | 0. Not obtained | 9 |
| 0 | 1. White/Caucasian | 1. Hispanic, Mexican | 1138 |
| 0 | 1. White/Caucasian | 2. Hispanic, Other | 677 |
| 0 | 1. White/Caucasian | 3. Hispanic, type unknown | 18 |
| 0 | 1. White/Caucasian | 5. Non-Hispanic | 11980 |
| 0 | 2. Black or African American | 1. Hispanic, Mexican | 10 |
| 0 | 2. Black or African American | 2. Hispanic, Other | 86 |
| 0 | 2. Black or African American | 3. Hispanic, type unknown | 2 |
| 0 | 7. Other | 0. Not obtained | 15 |
| 0 | 7. Other | 1. Hispanic, Mexican | 795 |
| 0 | 7. Other | 2. Hispanic, Other | 632 |
| 0 | 7. Other | 3. Hispanic, type unknown | 5 |
| 0 | 7. Other | 5. Non-Hispanic | 1030 |
| 1 | 2. Black or African American | 0. Not obtained | 12 |
| 1 | 2. Black or African American | 5. Non-Hispanic | 4422 |
| NA | NA | NA | 3 |
Hispanic came from the variable HISPANIC.
| Hispanic from trk2022tr_r | HISPANIC | n |
|---|---|---|
| 0 | 0. Not obtained | 41 |
| 0 | 5. Non-Hispanic | 17441 |
| 1 | 1. Hispanic, Mexican | 1983 |
| 1 | 2. Hispanic, Other | 1418 |
| 1 | 3. Hispanic, type unknown | 26 |
| NA | NA | 3 |
Recode the functional impairment items
This section shows the recoding of the IADL items.
There are 11 IADL items that are considered to measure functional impairment.
Item responses of 1 (Yes) and 6 (Can’t do) are recoded as “impairment”
Item responses of 5 (No), 7 (Don’t do), 8 (Don’t know) and 9 (Refused) are recoded as “no impairment”
Two of the items - “Difficulty taking medication” and “Do you think you would have difficulty taking medication” are logically dependent and are merged into one item
The sum of these 10 recoded IADL items is the functional impairment score
ADL: Difficulty Dressing
|
Total | |||
|---|---|---|---|---|
| Impaired | Not impaired | Unknown | ||
| DIFFICULTY- DRESSING | ||||
| 1 | 2,352 | 0 | 0 | 2,352 |
| 5 | 0 | 10,875 | 0 | 10,875 |
| 6 | 52 | 0 | 0 | 52 |
| 7 | 0 | 0 | 12 | 12 |
| 8 | 0 | 0 | 10 | 10 |
| 9 | 0 | 0 | 2 | 2 |
| Unknown | 0 | 0 | 7,609 | 7,609 |
| Total | 2,404 | 10,875 | 7,633 | 20,912 |
ADL: Difficulty Bathing
|
Total | |||
|---|---|---|---|---|
| Impaired | Not impaired | Unknown | ||
| DIFFICULTY BATHING | ||||
| 1 | 1,690 | 0 | 0 | 1,690 |
| 5 | 0 | 8,552 | 0 | 8,552 |
| 6 | 39 | 0 | 0 | 39 |
| 7 | 0 | 0 | 6 | 6 |
| 8 | 0 | 0 | 6 | 6 |
| 9 | 0 | 0 | 1 | 1 |
| Unknown | 0 | 0 | 10,618 | 10,618 |
| Total | 1,729 | 8,552 | 10,631 | 20,912 |
ADL: Difficulty Eating
|
Total | |||
|---|---|---|---|---|
| Impaired | Not impaired | Unknown | ||
| DIFFICULTY EATING | ||||
| 1 | 806 | 0 | 0 | 806 |
| 5 | 0 | 9,439 | 0 | 9,439 |
| 6 | 28 | 0 | 0 | 28 |
| 7 | 0 | 0 | 15 | 15 |
| 8 | 0 | 0 | 4 | 4 |
| 9 | 0 | 0 | 2 | 2 |
| Unknown | 0 | 0 | 10,618 | 10,618 |
| Total | 834 | 9,439 | 10,639 | 20,912 |
ADL: Difficulty Using Toilet
|
Total | |||
|---|---|---|---|---|
| Impaired | Not impaired | Unknown | ||
| DIFFICULTY USING TOILET | ||||
| 1 | 1,336 | 0 | 0 | 1,336 |
| 5 | 0 | 8,892 | 0 | 8,892 |
| 6 | 32 | 0 | 0 | 32 |
| 7 | 0 | 0 | 23 | 23 |
| 8 | 0 | 0 | 10 | 10 |
| 9 | 0 | 0 | 1 | 1 |
| Unknown | 0 | 0 | 10,618 | 10,618 |
| Total | 1,368 | 8,892 | 10,652 | 20,912 |
IADL: Difficulty Using Maps
|
Total | |||
|---|---|---|---|---|
| Impaired | Not impaired | Unknown | ||
| DIFFICULTY- USING MAPS | ||||
| 1 | 2,517 | 0 | 0 | 2,517 |
| 5 | 0 | 16,811 | 0 | 16,811 |
| 6 | 268 | 0 | 0 | 268 |
| 7 | 0 | 0 | 1,233 | 1,233 |
| 8 | 0 | 0 | 39 | 39 |
| 9 | 0 | 0 | 7 | 7 |
| Unknown | 0 | 0 | 37 | 37 |
| Total | 2,785 | 16,811 | 1,316 | 20,912 |
IADL: Difficulty Meal Prep
|
Total | |||
|---|---|---|---|---|
| Impaired | Not impaired | Unknown | ||
| IADL MEAL PREPARATION DIFFICULTY | ||||
| 1 | 1,207 | 0 | 0 | 1,207 |
| 5 | 0 | 18,880 | 0 | 18,880 |
| 6 | 101 | 0 | 0 | 101 |
| 7 | 0 | 0 | 679 | 679 |
| 8 | 0 | 0 | 5 | 5 |
| 9 | 0 | 0 | 3 | 3 |
| Unknown | 0 | 0 | 37 | 37 |
| Total | 1,308 | 18,880 | 724 | 20,912 |
IADL: Difficulty Grocery Shopping
|
Total | |||
|---|---|---|---|---|
| Impaired | Not impaired | Unknown | ||
| IADL GROC SHOP DIFFICULTY | ||||
| 1 | 1,761 | 0 | 0 | 1,761 |
| 5 | 0 | 18,324 | 0 | 18,324 |
| 6 | 81 | 0 | 0 | 81 |
| 7 | 0 | 0 | 701 | 701 |
| 8 | 0 | 0 | 6 | 6 |
| 9 | 0 | 0 | 2 | 2 |
| Unknown | 0 | 0 | 37 | 37 |
| Total | 1,842 | 18,324 | 746 | 20,912 |
IADL: Difficulty Making Phone Calls
|
Total | |||
|---|---|---|---|---|
| Impaired | Not impaired | Unknown | ||
| IADL MAKING PHONE CALLS DIFFICULTY | ||||
| 1 | 888 | 0 | 0 | 888 |
| 5 | 0 | 19,782 | 0 | 19,782 |
| 6 | 51 | 0 | 0 | 51 |
| 7 | 0 | 0 | 146 | 146 |
| 8 | 0 | 0 | 4 | 4 |
| 9 | 0 | 0 | 4 | 4 |
| Unknown | 0 | 0 | 37 | 37 |
| Total | 939 | 19,782 | 191 | 20,912 |
| IADL: Difficulty Taking Meds | IADL TAKING MEDICATION DIFFICULTY | IADL TAKING MEDS IF NEEDED DIFFICULTY | n |
|---|---|---|---|
| 0 | 5 | NA | 19671 |
| 0 | 7 | 5 | 202 |
| 1 | 1 | NA | 923 |
| 1 | 6 | NA | 24 |
| 1 | 7 | 1 | 41 |
| NA | 7 | 8 | 6 |
| NA | 8 | NA | 5 |
| NA | 9 | NA | 3 |
| NA | NA | NA | 37 |
IADL: Difficulty Managing Money
|
Total | |||
|---|---|---|---|---|
| Impaired | Not impaired | Unknown | ||
| IADL MANAGING MONEY DIFFICULTY | ||||
| 1 | 1,339 | 0 | 0 | 1,339 |
| 5 | 0 | 18,751 | 0 | 18,751 |
| 6 | 53 | 0 | 0 | 53 |
| 7 | 0 | 0 | 708 | 708 |
| 8 | 0 | 0 | 17 | 17 |
| 9 | 0 | 0 | 7 | 7 |
| Unknown | 0 | 0 | 37 | 37 |
| Total | 1,392 | 18,751 | 769 | 20,912 |
| Characteristic | N = 20,9121 |
|---|---|
| ADL: Difficulty Dressing | |
| Impaired | 2,404 (18%) |
| Not impaired | 10,875 (82%) |
| Unknown | 7,633 |
| ADL: Difficulty Bathing | |
| Impaired | 1,729 (17%) |
| Not impaired | 8,552 (83%) |
| Unknown | 10,631 |
| ADL: Difficulty Eating | |
| Impaired | 834 (8.1%) |
| Not impaired | 9,439 (92%) |
| Unknown | 10,639 |
| ADL: Difficulty Using Toilet | |
| Impaired | 1,368 (13%) |
| Not impaired | 8,892 (87%) |
| Unknown | 10,652 |
| IADL: Difficulty Using Maps | |
| Impaired | 2,785 (14%) |
| Not impaired | 16,811 (86%) |
| Unknown | 1,316 |
| IADL: Difficulty Meal Prep | |
| Impaired | 1,308 (6.5%) |
| Not impaired | 18,880 (94%) |
| Unknown | 724 |
| IADL: Difficulty Grocery Shopping | |
| Impaired | 1,842 (9.1%) |
| Not impaired | 18,324 (91%) |
| Unknown | 746 |
| IADL: Difficulty Making Phone Calls | |
| Impaired | 939 (4.5%) |
| Not impaired | 19,782 (95%) |
| Unknown | 191 |
| IADL: Difficulty Taking Meds | |
| Impaired | 988 (4.7%) |
| Not impaired | 19,873 (95%) |
| Unknown | 51 |
| IADL: Difficulty Managing Money | |
| Impaired | 1,392 (6.9%) |
| Not impaired | 18,751 (93%) |
| Unknown | 769 |
| Sum of ADL/IADL impairments | |
| 0 | 14,821 (71%) |
| 1 | 2,946 (14%) |
| 2 | 1,033 (4.9%) |
| 3 | 651 (3.1%) |
| 4 | 446 (2.1%) |
| 5 | 304 (1.5%) |
| 6 | 244 (1.2%) |
| 7 | 151 (0.7%) |
| 8 | 131 (0.6%) |
| 9 | 99 (0.5%) |
| 10 | 86 (0.4%) |
| 1 n (%) | |
Recode the informant based items
This section shows the recoding of the Jorm items and the subjective cognitive impairment item.
There are 16 Jorm items.
- For each Jorm item, three HRS items need to be combined to recreate the Jorm item
The average of these 16 Jorms items is the score
The subjective cognitive impairment item was recoded so that “no change” and “improvement” was combined into one category.
| Remembering things about family | RATE R AT REMEMBERING THINGS- PC | ORGANIZATION IMPROVED- PC | ORGANIZATION WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 8 |
| 2 | 1 | 2 | NA | 8 |
| 3 | 2 | NA | NA | 377 |
| 4 | 3 | NA | 4 | 111 |
| 5 | 3 | NA | 5 | 213 |
| NA | 3 | NA | 8 | 2 |
| NA | 4 | NA | NA | 2 |
| NA | NA | NA | NA | 20191 |
| Remembering things that happened recently | RATE R AT REMEMBERING RECENT EVENTS- PC | REMEMBERING RECENT EVENTS IMPROVED- PC | REMEMBERING RECENT EVENTS WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 8 |
| 2 | 1 | 2 | NA | 6 |
| 3 | 2 | NA | NA | 352 |
| 4 | 3 | NA | 4 | 116 |
| 5 | 3 | NA | 5 | 234 |
| NA | 3 | NA | 8 | 1 |
| NA | 4 | NA | NA | 3 |
| NA | 9 | NA | NA | 1 |
| NA | NA | NA | NA | 20191 |
| Recalling conversations a few day later | RATE R AT CONVERSATION RECALL- PC | CONVERSATION RECALL IMPROVED- PC | CONVERSATION RECALL WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 6 |
| 2 | 1 | 2 | NA | 4 |
| 3 | 2 | NA | NA | 353 |
| 4 | 3 | NA | 4 | 124 |
| 5 | 3 | NA | 5 | 222 |
| NA | 1 | 9 | NA | 1 |
| NA | 4 | NA | NA | 10 |
| NA | 8 | NA | NA | 1 |
| NA | NA | NA | NA | 20191 |
| Remembering telephone number | RATE REMEMBERING OWN PHONE NUM- PC | REMEMBERING OWN PHONE NUM IMPROVE- PC | REMEMBERING OWN PHONE NUM WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 7 |
| 2 | 1 | 2 | NA | 4 |
| 3 | 2 | NA | NA | 425 |
| 4 | 3 | NA | 4 | 58 |
| 5 | 3 | NA | 5 | 192 |
| NA | 4 | NA | NA | 31 |
| NA | 8 | NA | NA | 3 |
| NA | 9 | NA | NA | 1 |
| NA | NA | NA | NA | 20191 |
| Remembering day and month | RATE REMEMBERING CURRENT DY/MO- PC | REMEMBERING CURRENT DY/MO IMPROVE- PC | REMEMBERING CURRENT DY/MO WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 5 |
| 2 | 1 | 2 | NA | 4 |
| 3 | 2 | NA | NA | 391 |
| 4 | 3 | NA | 4 | 91 |
| 5 | 3 | NA | 5 | 206 |
| NA | 4 | NA | NA | 21 |
| NA | 8 | NA | NA | 2 |
| NA | 9 | NA | NA | 1 |
| NA | NA | NA | NA | 20191 |
| Remembering where things are kept | RATE REMEMBERING WHERE THINGS KEPT- PC | WHERE THINGS ARE KEPT IMPROVED- PC | WHERE THINGS ARE KEPT WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 3 |
| 2 | 1 | 2 | NA | 4 |
| 3 | 2 | NA | NA | 415 |
| 4 | 3 | NA | 4 | 91 |
| 5 | 3 | NA | 5 | 185 |
| NA | 4 | NA | NA | 20 |
| NA | 8 | NA | NA | 1 |
| NA | 9 | NA | NA | 2 |
| NA | NA | NA | NA | 20191 |
| Remembering where to find things | RATE FINDING THINGS IN DIFF PLACES- PC | FINDING THINGS IMPROVED- PC | FINDING THINGS WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 5 |
| 2 | 1 | 2 | NA | 6 |
| 3 | 2 | NA | NA | 343 |
| 4 | 3 | NA | 4 | 113 |
| 5 | 3 | NA | 5 | 218 |
| NA | 1 | 9 | NA | 1 |
| NA | 3 | NA | 8 | 1 |
| NA | 4 | NA | NA | 29 |
| NA | 8 | NA | NA | 4 |
| NA | 9 | NA | NA | 1 |
| NA | NA | NA | NA | 20191 |
| Knowing how to work familar machines around the house | RATE WORKING WITH FAMILIAR MACHINES- PC | WORKING WITH FAMILIAR MACHINES IMPR- PC | WORKING WITH FAMILIAR MACHINES WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 6 |
| 2 | 1 | 2 | NA | 6 |
| 3 | 2 | NA | NA | 371 |
| 4 | 3 | NA | 4 | 57 |
| 5 | 3 | NA | 5 | 167 |
| NA | 4 | NA | NA | 111 |
| NA | 9 | NA | NA | 3 |
| NA | NA | NA | NA | 20191 |
| Learning to use a new gadget | RATE LEARNING NEW MACHINES- PC | LEARNING NEW MACHINES IMPROVED- PC | LEARNING NEW MACHINES WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 6 |
| 2 | 1 | 2 | NA | 8 |
| 3 | 2 | NA | NA | 317 |
| 4 | 3 | NA | 4 | 60 |
| 5 | 3 | NA | 5 | 198 |
| NA | 4 | NA | NA | 130 |
| NA | 9 | NA | NA | 2 |
| NA | NA | NA | NA | 20191 |
| Learning new things in general | RATE LEARNING NEW THINGS IN GENERAL- PC | LEARNING ABILITY IMPROVE- PC | LEARNING ABILITY WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 12 |
| 2 | 1 | 2 | NA | 9 |
| 3 | 2 | NA | 4 | 1 |
| 3 | 2 | NA | NA | 349 |
| 4 | 3 | NA | 4 | 73 |
| 5 | 3 | NA | 5 | 214 |
| NA | 3 | NA | 8 | 1 |
| NA | 4 | NA | NA | 57 |
| NA | 8 | NA | NA | 2 |
| NA | 9 | NA | NA | 3 |
| NA | NA | NA | NA | 20191 |
| Following a story in a book or on TV | RATE ABILITY TO FOLLOW STORY- PC | ABILITY TO FOLLOW STORY IMPROVE- PC | ABILITY TO FOLLOW STORY WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 13 |
| 2 | 1 | 2 | NA | 6 |
| 3 | 2 | NA | NA | 403 |
| 4 | 3 | NA | 4 | 77 |
| 5 | 3 | NA | 5 | 175 |
| NA | 1 | 9 | NA | 1 |
| NA | 3 | NA | 8 | 1 |
| NA | 4 | NA | NA | 37 |
| NA | 8 | NA | NA | 6 |
| NA | 9 | NA | NA | 2 |
| NA | NA | NA | NA | 20191 |
| Making decisions on everyday matters | RATE MAKING DECISIONS- PC | MAKE DECISIONS IMPROVE- PC | MAKE DECISIONS WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 7 |
| 2 | 1 | 2 | NA | 6 |
| 3 | 2 | NA | NA | 380 |
| 4 | 3 | NA | 4 | 85 |
| 5 | 3 | NA | 5 | 186 |
| NA | 4 | NA | NA | 55 |
| NA | 9 | NA | NA | 2 |
| NA | NA | NA | NA | 20191 |
| Handling money for shopping | RATE HANDLING SHOPPING MONEY- PC | HANDLING SHOPPING MONEY IMPROVE- PC | HANDLING SHOPPING MONEY WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 6 |
| 2 | 1 | 2 | NA | 3 |
| 3 | 2 | NA | NA | 335 |
| 4 | 3 | NA | 4 | 37 |
| 5 | 3 | NA | 5 | 141 |
| NA | 4 | NA | NA | 198 |
| NA | 9 | NA | NA | 1 |
| NA | NA | NA | NA | 20191 |
| Handling financial matters | RATE HANDLING FINANCES- PC | HANDLING FINANCES IMPROVE- PC | HANDLING FINANCES WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 5 |
| 2 | 1 | 2 | NA | 6 |
| 3 | 2 | NA | NA | 289 |
| 4 | 3 | NA | 4 | 32 |
| 5 | 3 | NA | 5 | 139 |
| NA | 4 | NA | NA | 249 |
| NA | 9 | NA | NA | 1 |
| NA | NA | NA | NA | 20191 |
| Handling everyday arthimetic problems | RATE HANDLING DAILY ARITHMETIC PROBS- PC | HANDLING ARITHMETIC PROBLEMS IMPROVE- PC | HANDLING ARITHMETIC PROBLEMS WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 8 |
| 2 | 1 | 2 | NA | 6 |
| 3 | 2 | NA | NA | 357 |
| 4 | 3 | NA | 4 | 47 |
| 5 | 3 | NA | 5 | 178 |
| NA | 4 | NA | NA | 122 |
| NA | 8 | NA | NA | 2 |
| NA | 9 | NA | NA | 1 |
| NA | NA | NA | NA | 20191 |
| Using intelligence to understand what's going on | RATE REASONING- PC | REASONING IMPROVE- PC | REASONING WORSE- PC | n |
|---|---|---|---|---|
| 1 | 1 | 1 | NA | 7 |
| 2 | 1 | 2 | NA | 12 |
| 3 | 2 | NA | NA | 384 |
| 4 | 3 | NA | 4 | 92 |
| 5 | 3 | NA | 5 | 189 |
| NA | 3 | NA | 8 | 1 |
| NA | 4 | NA | NA | 32 |
| NA | 8 | NA | NA | 3 |
| NA | 9 | NA | NA | 1 |
| NA | NA | NA | NA | 20191 |
| Characteristic | N = 20,912 |
|---|---|
| Jorm score (HRS) | |
| Mean (SD) | 3.72 (0.83) |
| Median (Q1, Q3) | 3.38 (3.00, 4.56) |
| N Non-missing | 721 |
| Unknown | 20,191 |
| Jorm score (HRS HCAP variable set 3) | |
| Mean (SD) | 3.18 (0.55) |
| Median (Q1, Q3) | 3.04 (3.00, 3.31) |
| N Non-missing | 3,496 |
| Unknown | 17,416 |
| Characteristic | N = 20,9121 |
|---|---|
| Compared to two years ago, would you say your memory is better now, about the same, or worse now than it was then? | |
| Worse | 4,315 / 19,938 (22%) |
| Same/Better | 15,623 / 19,938 (78%) |
| Unknown | 974 |
| 1 n / N (%) | |
Recode cognitive items
This section shows the recoding of the cognitive items.
In general, I tried to have higher values indicate correct or better performance.
I recoded “don’t know” and “refusal” to incorrect. These could be changed.
If all the component values of a vd variable are NA, then I set the vd variable to NA.
The tables show the recoded variable on the left column, the original variable in the center, and the sample size on the right column.
Orientation
The number correct of the four orientation to time questions - Month, Day, Year and Day of week.
| Orientation to Time - number correct | PD151 | PD152 | PD153 | PD154 | n |
|---|---|---|---|---|---|
| 0 | 5. MONTH NOT OK | 5. DAY OF MONTH NOT OK | 5. YEAR NOT OK | 5. DAY NOT OK | 34 |
| 0 | 5. MONTH NOT OK | 5. DAY OF MONTH NOT OK | 5. YEAR NOT OK | 8. DK (Don't Know) | 2 |
| 0 | 5. MONTH NOT OK | 5. DAY OF MONTH NOT OK | 8. DK (Don't Know) | 5. DAY NOT OK | 6 |
| 0 | 5. MONTH NOT OK | 5. DAY OF MONTH NOT OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 1 |
| 0 | 5. MONTH NOT OK | 8. DK (Don't Know) | 5. YEAR NOT OK | 5. DAY NOT OK | 1 |
| 0 | 5. MONTH NOT OK | 8. DK (Don't Know) | 5. YEAR NOT OK | 8. DK (Don't Know) | 1 |
| 0 | 5. MONTH NOT OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 5. DAY NOT OK | 5 |
| 0 | 5. MONTH NOT OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 8. DK (Don't Know) | 2 |
| 0 | 8. DK (Don't Know) | 5. DAY OF MONTH NOT OK | 5. YEAR NOT OK | 5. DAY NOT OK | 2 |
| 0 | 8. DK (Don't Know) | 5. DAY OF MONTH NOT OK | 5. YEAR NOT OK | 8. DK (Don't Know) | 1 |
| 0 | 8. DK (Don't Know) | 5. DAY OF MONTH NOT OK | 8. DK (Don't Know) | 5. DAY NOT OK | 1 |
| 0 | 8. DK (Don't Know) | 8. DK (Don't Know) | 5. YEAR NOT OK | 5. DAY NOT OK | 4 |
| 0 | 8. DK (Don't Know) | 8. DK (Don't Know) | 5. YEAR NOT OK | 8. DK (Don't Know) | 1 |
| 0 | 8. DK (Don't Know) | 8. DK (Don't Know) | 8. DK (Don't Know) | 5. DAY NOT OK | 16 |
| 0 | 8. DK (Don't Know) | 8. DK (Don't Know) | 8. DK (Don't Know) | 8. DK (Don't Know) | 46 |
| 0 | 9. RF (Refused) | 9. RF (Refused) | 9. RF (Refused) | 5. DAY NOT OK | 1 |
| 0 | 9. RF (Refused) | 9. RF (Refused) | 9. RF (Refused) | 9. RF (Refused) | 14 |
| 1 | 1. MONTH OK | 5. DAY OF MONTH NOT OK | 5. YEAR NOT OK | 5. DAY NOT OK | 35 |
| 1 | 1. MONTH OK | 5. DAY OF MONTH NOT OK | 5. YEAR NOT OK | 8. DK (Don't Know) | 1 |
| 1 | 1. MONTH OK | 5. DAY OF MONTH NOT OK | 8. DK (Don't Know) | 5. DAY NOT OK | 3 |
| 1 | 1. MONTH OK | 5. DAY OF MONTH NOT OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 1 |
| 1 | 1. MONTH OK | 5. DAY OF MONTH NOT OK | 9. RF (Refused) | 5. DAY NOT OK | 1 |
| 1 | 1. MONTH OK | 8. DK (Don't Know) | 5. YEAR NOT OK | 5. DAY NOT OK | 4 |
| 1 | 1. MONTH OK | 8. DK (Don't Know) | 5. YEAR NOT OK | 8. DK (Don't Know) | 3 |
| 1 | 1. MONTH OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 5. DAY NOT OK | 3 |
| 1 | 1. MONTH OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 8. DK (Don't Know) | 7 |
| 1 | 5. MONTH NOT OK | 1. DAY OF MONTH OK | 5. YEAR NOT OK | 5. DAY NOT OK | 5 |
| 1 | 5. MONTH NOT OK | 1. DAY OF MONTH OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 2 |
| 1 | 5. MONTH NOT OK | 5. DAY OF MONTH NOT OK | 1. YEAR OK | 5. DAY NOT OK | 12 |
| 1 | 5. MONTH NOT OK | 5. DAY OF MONTH NOT OK | 1. YEAR OK | 8. DK (Don't Know) | 2 |
| 1 | 5. MONTH NOT OK | 5. DAY OF MONTH NOT OK | 5. YEAR NOT OK | 1. DAY OK | 39 |
| 1 | 5. MONTH NOT OK | 5. DAY OF MONTH NOT OK | 8. DK (Don't Know) | 1. DAY OK | 4 |
| 1 | 5. MONTH NOT OK | 8. DK (Don't Know) | 1. YEAR OK | 5. DAY NOT OK | 1 |
| 1 | 5. MONTH NOT OK | 8. DK (Don't Know) | 1. YEAR OK | 8. DK (Don't Know) | 1 |
| 1 | 5. MONTH NOT OK | 8. DK (Don't Know) | 5. YEAR NOT OK | 1. DAY OK | 3 |
| 1 | 5. MONTH NOT OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 1. DAY OK | 6 |
| 1 | 8. DK (Don't Know) | 1. DAY OF MONTH OK | 5. YEAR NOT OK | 5. DAY NOT OK | 1 |
| 1 | 8. DK (Don't Know) | 1. DAY OF MONTH OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 1 |
| 1 | 8. DK (Don't Know) | 5. DAY OF MONTH NOT OK | 1. YEAR OK | 8. DK (Don't Know) | 1 |
| 1 | 8. DK (Don't Know) | 5. DAY OF MONTH NOT OK | 8. DK (Don't Know) | 1. DAY OK | 1 |
| 1 | 8. DK (Don't Know) | 8. DK (Don't Know) | 1. YEAR OK | 5. DAY NOT OK | 2 |
| 1 | 8. DK (Don't Know) | 8. DK (Don't Know) | 1. YEAR OK | 8. DK (Don't Know) | 2 |
| 1 | 8. DK (Don't Know) | 8. DK (Don't Know) | 5. YEAR NOT OK | 1. DAY OK | 4 |
| 1 | 8. DK (Don't Know) | 8. DK (Don't Know) | 8. DK (Don't Know) | 1. DAY OK | 26 |
| 2 | 1. MONTH OK | 1. DAY OF MONTH OK | 5. YEAR NOT OK | 5. DAY NOT OK | 26 |
| 2 | 1. MONTH OK | 1. DAY OF MONTH OK | 5. YEAR NOT OK | 8. DK (Don't Know) | 2 |
| 2 | 1. MONTH OK | 1. DAY OF MONTH OK | 8. DK (Don't Know) | 5. DAY NOT OK | 2 |
| 2 | 1. MONTH OK | 1. DAY OF MONTH OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 1 |
| 2 | 1. MONTH OK | 5. DAY OF MONTH NOT OK | 1. YEAR OK | 5. DAY NOT OK | 99 |
| 2 | 1. MONTH OK | 5. DAY OF MONTH NOT OK | 1. YEAR OK | 8. DK (Don't Know) | 10 |
| 2 | 1. MONTH OK | 5. DAY OF MONTH NOT OK | 5. YEAR NOT OK | 1. DAY OK | 71 |
| 2 | 1. MONTH OK | 5. DAY OF MONTH NOT OK | 8. DK (Don't Know) | 1. DAY OK | 12 |
| 2 | 1. MONTH OK | 8. DK (Don't Know) | 1. YEAR OK | 5. DAY NOT OK | 9 |
| 2 | 1. MONTH OK | 8. DK (Don't Know) | 1. YEAR OK | 8. DK (Don't Know) | 6 |
| 2 | 1. MONTH OK | 8. DK (Don't Know) | 5. YEAR NOT OK | 1. DAY OK | 6 |
| 2 | 1. MONTH OK | 8. DK (Don't Know) | 8. DK (Don't Know) | 1. DAY OK | 7 |
| 2 | 5. MONTH NOT OK | 1. DAY OF MONTH OK | 1. YEAR OK | 5. DAY NOT OK | 4 |
| 2 | 5. MONTH NOT OK | 1. DAY OF MONTH OK | 5. YEAR NOT OK | 1. DAY OK | 10 |
| 2 | 5. MONTH NOT OK | 1. DAY OF MONTH OK | 8. DK (Don't Know) | 1. DAY OK | 2 |
| 2 | 5. MONTH NOT OK | 5. DAY OF MONTH NOT OK | 1. YEAR OK | 1. DAY OK | 111 |
| 2 | 5. MONTH NOT OK | 8. DK (Don't Know) | 1. YEAR OK | 1. DAY OK | 7 |
| 2 | 8. DK (Don't Know) | 1. DAY OF MONTH OK | 1. YEAR OK | 8. DK (Don't Know) | 1 |
| 2 | 8. DK (Don't Know) | 1. DAY OF MONTH OK | 5. YEAR NOT OK | 1. DAY OK | 3 |
| 2 | 8. DK (Don't Know) | 1. DAY OF MONTH OK | 8. DK (Don't Know) | 1. DAY OK | 3 |
| 2 | 8. DK (Don't Know) | 5. DAY OF MONTH NOT OK | 1. YEAR OK | 1. DAY OK | 9 |
| 2 | 8. DK (Don't Know) | 8. DK (Don't Know) | 1. YEAR OK | 1. DAY OK | 19 |
| 3 | 1. MONTH OK | 1. DAY OF MONTH OK | 1. YEAR OK | 5. DAY NOT OK | 189 |
| 3 | 1. MONTH OK | 1. DAY OF MONTH OK | 1. YEAR OK | 8. DK (Don't Know) | 15 |
| 3 | 1. MONTH OK | 1. DAY OF MONTH OK | 5. YEAR NOT OK | 1. DAY OK | 136 |
| 3 | 1. MONTH OK | 1. DAY OF MONTH OK | 8. DK (Don't Know) | 1. DAY OK | 16 |
| 3 | 1. MONTH OK | 5. DAY OF MONTH NOT OK | 1. YEAR OK | 1. DAY OK | 1834 |
| 3 | 1. MONTH OK | 8. DK (Don't Know) | 1. YEAR OK | 1. DAY OK | 137 |
| 3 | 1. MONTH OK | 9. RF (Refused) | 1. YEAR OK | 1. DAY OK | 1 |
| 3 | 5. MONTH NOT OK | 1. DAY OF MONTH OK | 1. YEAR OK | 1. DAY OK | 144 |
| 3 | 8. DK (Don't Know) | 1. DAY OF MONTH OK | 1. YEAR OK | 1. DAY OK | 9 |
| 4 | 1. MONTH OK | 1. DAY OF MONTH OK | 1. YEAR OK | 1. DAY OK | 10490 |
| NA | NA | NA | NA | NA | 7212 |
| Characteristic | N = 20,9121 |
|---|---|
| Orientation to Time - number correct | |
| 0 | 138 (1.0%) |
| 1 | 171 (1.2%) |
| 2 | 420 (3.1%) |
| 3 | 2,481 (18%) |
| 4 | 10,490 (77%) |
| Unknown | 7,212 |
| 1 n (%) | |
Count backwards
| Count backwards from 20 | PD124 | PD129 | n |
|---|---|---|---|
| 0 | 5. INCORRECT | NA | 1324 |
| 0 | 6. WANTS TO START OVER | 5. INCORRECT | 11 |
| 0 | 6. WANTS TO START OVER | 9. RF (Refused) | 2 |
| 0 | 9. RF (Refused) | NA | 20 |
| 1 | 1. CORRECT | NA | 18519 |
| 1 | 6. WANTS TO START OVER | 1. CORRECT | 35 |
| NA | NA | NA | 1001 |
| Characteristic | N = 20,9121 |
|---|---|
| Count backwards from 20 | |
| Correct | 18,554 (93%) |
| Incorrect | 1,357 (6.8%) |
| Unknown | 1,001 |
| 1 n (%) | |
Object naming
The scissors and cactus naming items have been combined into one item.
| Object naming - Scissors, cactus | PD155 | PD156 | n |
|---|---|---|---|
| 0 | 5. NOT CORRECT | 5. NOT CORRECT | 20 |
| 0 | 5. NOT CORRECT | 8. DK (Don't Know) | 20 |
| 0 | 5. NOT CORRECT | 9. RF (Refused) | 1 |
| 0 | 8. DK (Don't Know) | 5. NOT CORRECT | 9 |
| 0 | 8. DK (Don't Know) | 8. DK (Don't Know) | 27 |
| 0 | 9. RF (Refused) | 9. RF (Refused) | 17 |
| 1 | 1. SCISSORS OR SHEARS ONLY | 5. NOT CORRECT | 367 |
| 1 | 1. SCISSORS OR SHEARS ONLY | 8. DK (Don't Know) | 643 |
| 1 | 1. SCISSORS OR SHEARS ONLY | 9. RF (Refused) | 4 |
| 1 | 5. NOT CORRECT | 1. CACTUS OR NAME OF KIND OF CACTUS | 99 |
| 1 | 8. DK (Don't Know) | 1. CACTUS OR NAME OF KIND OF CACTUS | 28 |
| 1 | 9. RF (Refused) | 1. CACTUS OR NAME OF KIND OF CACTUS | 3 |
| 2 | 1. SCISSORS OR SHEARS ONLY | 1. CACTUS OR NAME OF KIND OF CACTUS | 12462 |
| NA | NA | NA | 7212 |
| Characteristic | N = 20,9121 |
|---|---|
| Object naming - Scissors, cactus | |
| 0 | 94 (0.7%) |
| 1 | 1,144 (8.4%) |
| 2 | 12,462 (91%) |
| Unknown | 7,212 |
| 1 n (%) | |
President/Vice-president
The president and vice-president naming items have been combined into one item.
| Naming - President, Vice-president | PD157 | PD158 | n |
|---|---|---|---|
| 0 | 5. NOT CORRECT | 5. NOT CORRECT | 51 |
| 0 | 5. NOT CORRECT | 8. DK (Don't Know) | 45 |
| 0 | 8. DK (Don't Know) | 5. NOT CORRECT | 11 |
| 0 | 8. DK (Don't Know) | 8. DK (Don't Know) | 241 |
| 0 | 8. DK (Don't Know) | 9. RF (Refused) | 1 |
| 0 | 9. RF (Refused) | 8. DK (Don't Know) | 4 |
| 0 | 9. RF (Refused) | 9. RF (Refused) | 23 |
| 1 | 1. LAST NAME CORRECT | 5. NOT CORRECT | 1343 |
| 1 | 1. LAST NAME CORRECT | 8. DK (Don't Know) | 3347 |
| 1 | 1. LAST NAME CORRECT | 9. RF (Refused) | 15 |
| 1 | 5. NOT CORRECT | 1. LAST NAME CORRECT | 14 |
| 1 | 8. DK (Don't Know) | 1. LAST NAME CORRECT | 15 |
| 1 | 9. RF (Refused) | 1. LAST NAME CORRECT | 4 |
| 2 | 1. LAST NAME CORRECT | 1. LAST NAME CORRECT | 8585 |
| NA | NA | NA | 7213 |
| Characteristic | N = 20,9121 |
|---|---|
| Naming - President, Vice-president | |
| 0 | 376 (2.7%) |
| 1 | 4,738 (35%) |
| 2 | 8,585 (63%) |
| Unknown | 7,213 |
| 1 n (%) | |
Serial 7
The new variable counts the number of correct responses on the serial 7. Each item was checked so that the difference between consecutive items is 7. So if a mistake was made early on, there is an opportunity to get the remaining items correct.
| Serial 7's - Number correct | rPD142 | rPD143 | rPD144 | rPD145 | rPD146 | n |
|---|---|---|---|---|---|---|
| 0 | 0. Incorrect | 0. Incorrect | 0. Incorrect | 0. Incorrect | 0. Incorrect | 406 |
| 0 | 0. Incorrect | 0. Incorrect | 0. Incorrect | 0. Incorrect | NA | 43 |
| 0 | 0. Incorrect | 0. Incorrect | 0. Incorrect | NA | NA | 116 |
| 0 | 0. Incorrect | 0. Incorrect | NA | NA | NA | 266 |
| 0 | 0. Incorrect | NA | NA | NA | NA | 1014 |
| 1 | 0. Incorrect | 0. Incorrect | 0. Incorrect | 0. Incorrect | 1. Correct | 58 |
| 1 | 0. Incorrect | 0. Incorrect | 0. Incorrect | 1. Correct | 0. Incorrect | 69 |
| 1 | 0. Incorrect | 0. Incorrect | 1. Correct | 0. Incorrect | 0. Incorrect | 68 |
| 1 | 0. Incorrect | 0. Incorrect | 1. Correct | 0. Incorrect | NA | 13 |
| 1 | 0. Incorrect | 1. Correct | 0. Incorrect | 0. Incorrect | 0. Incorrect | 91 |
| 1 | 0. Incorrect | 1. Correct | 0. Incorrect | 0. Incorrect | NA | 14 |
| 1 | 0. Incorrect | 1. Correct | 0. Incorrect | NA | NA | 31 |
| 1 | 1. Correct | 0. Incorrect | 0. Incorrect | 0. Incorrect | 0. Incorrect | 698 |
| 1 | 1. Correct | 0. Incorrect | 0. Incorrect | 0. Incorrect | NA | 82 |
| 1 | 1. Correct | 0. Incorrect | 0. Incorrect | NA | NA | 235 |
| 1 | 1. Correct | 0. Incorrect | NA | NA | NA | 754 |
| 2 | 0. Incorrect | 0. Incorrect | 0. Incorrect | 1. Correct | 1. Correct | 36 |
| 2 | 0. Incorrect | 0. Incorrect | 1. Correct | 0. Incorrect | 1. Correct | 48 |
| 2 | 0. Incorrect | 0. Incorrect | 1. Correct | 1. Correct | 0. Incorrect | 49 |
| 2 | 0. Incorrect | 1. Correct | 0. Incorrect | 0. Incorrect | 1. Correct | 24 |
| 2 | 0. Incorrect | 1. Correct | 0. Incorrect | 1. Correct | 0. Incorrect | 42 |
| 2 | 0. Incorrect | 1. Correct | 1. Correct | 0. Incorrect | 0. Incorrect | 46 |
| 2 | 0. Incorrect | 1. Correct | 1. Correct | 0. Incorrect | NA | 18 |
| 2 | 1. Correct | 0. Incorrect | 0. Incorrect | 0. Incorrect | 1. Correct | 295 |
| 2 | 1. Correct | 0. Incorrect | 0. Incorrect | 1. Correct | 0. Incorrect | 266 |
| 2 | 1. Correct | 0. Incorrect | 1. Correct | 0. Incorrect | 0. Incorrect | 249 |
| 2 | 1. Correct | 0. Incorrect | 1. Correct | 0. Incorrect | NA | 52 |
| 2 | 1. Correct | 1. Correct | 0. Incorrect | 0. Incorrect | 0. Incorrect | 327 |
| 2 | 1. Correct | 1. Correct | 0. Incorrect | 0. Incorrect | NA | 67 |
| 2 | 1. Correct | 1. Correct | 0. Incorrect | NA | NA | 179 |
| 3 | 0. Incorrect | 0. Incorrect | 1. Correct | 1. Correct | 1. Correct | 72 |
| 3 | 0. Incorrect | 1. Correct | 0. Incorrect | 1. Correct | 1. Correct | 36 |
| 3 | 0. Incorrect | 1. Correct | 1. Correct | 0. Incorrect | 1. Correct | 26 |
| 3 | 0. Incorrect | 1. Correct | 1. Correct | 1. Correct | 0. Incorrect | 52 |
| 3 | 1. Correct | 0. Incorrect | 0. Incorrect | 1. Correct | 1. Correct | 553 |
| 3 | 1. Correct | 0. Incorrect | 1. Correct | 0. Incorrect | 1. Correct | 274 |
| 3 | 1. Correct | 0. Incorrect | 1. Correct | 1. Correct | 0. Incorrect | 633 |
| 3 | 1. Correct | 1. Correct | 0. Incorrect | 0. Incorrect | 1. Correct | 333 |
| 3 | 1. Correct | 1. Correct | 0. Incorrect | 1. Correct | 0. Incorrect | 389 |
| 3 | 1. Correct | 1. Correct | 1. Correct | 0. Incorrect | 0. Incorrect | 264 |
| 3 | 1. Correct | 1. Correct | 1. Correct | 0. Incorrect | NA | 44 |
| 4 | 0. Incorrect | 1. Correct | 1. Correct | 1. Correct | 1. Correct | 107 |
| 4 | 1. Correct | 0. Incorrect | 1. Correct | 1. Correct | 1. Correct | 1417 |
| 4 | 1. Correct | 1. Correct | 0. Incorrect | 1. Correct | 1. Correct | 871 |
| 4 | 1. Correct | 1. Correct | 1. Correct | 0. Incorrect | 1. Correct | 493 |
| 4 | 1. Correct | 1. Correct | 1. Correct | 1. Correct | 0. Incorrect | 851 |
| 5 | 1. Correct | 1. Correct | 1. Correct | 1. Correct | 1. Correct | 7899 |
| NA | NA | NA | NA | NA | NA | 942 |
| Characteristic | N = 20,9121 |
|---|---|
| Serial 7's - Number correct | |
| 0 | 1,845 (9.2%) |
| 1 | 2,113 (11%) |
| 2 | 1,698 (8.5%) |
| 3 | 2,676 (13%) |
| 4 | 3,739 (19%) |
| 5 | 7,899 (40%) |
| Unknown | 942 |
| 1 n (%) | |
Animal naming
This variable is the number of correct responses minus the number of errors. Missing values are set at the median value of errors.
| Animal naming (correct - errors) | TOTAL ANIMALS ANSWERS | ANIMAL MISTAKES NUMBER | n |
|---|---|---|---|
| 0 | 0 | NA | 12 |
| 0 | 1 | 3 | 1 |
| 0 | 1 | NA | 18 |
| 0 | 2 | 2 | 1 |
| 0 | 3 | 3 | 2 |
| 0 | 6 | 7 | 1 |
| 0 | 7 | 18 | 1 |
| 0 | 8 | 15 | 1 |
| 0 | 8 | 16 | 1 |
| 0 | 9 | 10 | 2 |
| 0 | 10 | 10 | 1 |
| 0 | 10 | 11 | 1 |
| 0 | 11 | 11 | 1 |
| 0 | 12 | 15 | 1 |
| 0 | 13 | 15 | 1 |
| 0 | 13 | 24 | 1 |
| 0 | 14 | 14 | 1 |
| 0 | 14 | 17 | 1 |
| 0 | 15 | 15 | 1 |
| 0 | 15 | 22 | 1 |
| 0 | 16 | 22 | 1 |
| 0 | 17 | 21 | 1 |
| 0 | 17 | 24 | 1 |
| 0 | 17 | 29 | 1 |
| 0 | 17 | 87 | 1 |
| 0 | 18 | 20 | 1 |
| 0 | 18 | 22 | 1 |
| 0 | 20 | 25 | 1 |
| 0 | 20 | 37 | 1 |
| 0 | 21 | 55 | 1 |
| 0 | 22 | 28 | 1 |
| 0 | 25 | 38 | 1 |
| 0 | 25 | 42 | 1 |
| 0 | 25 | 54 | 1 |
| 0 | 26 | 42 | 1 |
| 0 | 26 | 93 | 1 |
| 0 | 30 | 45 | 1 |
| 0 | 40 | 45 | 1 |
| 1 | 2 | 1 | 2 |
| 1 | 2 | NA | 26 |
| 1 | 4 | 3 | 1 |
| 1 | 6 | 5 | 2 |
| 1 | 9 | 8 | 1 |
| 1 | 11 | 10 | 1 |
| 1 | 17 | 16 | 1 |
| 1 | 20 | 19 | 1 |
| 2 | 2 | 0 | 1 |
| 2 | 3 | 1 | 5 |
| 2 | 3 | NA | 54 |
| 2 | 4 | 2 | 4 |
| 2 | 5 | 3 | 2 |
| 2 | 6 | 4 | 1 |
| 2 | 7 | 5 | 1 |
| 2 | 8 | 6 | 1 |
| 2 | 17 | 15 | 1 |
| 3 | 4 | 1 | 8 |
| 3 | 4 | NA | 89 |
| 3 | 5 | 2 | 6 |
| 3 | 8 | 5 | 1 |
| 3 | 9 | 6 | 1 |
| 3 | 11 | 8 | 1 |
| 3 | 13 | 10 | 1 |
| 3 | 23 | 20 | 1 |
| 3 | 28 | 25 | 1 |
| 4 | 5 | 1 | 12 |
| 4 | 5 | NA | 131 |
| 4 | 6 | 2 | 4 |
| 4 | 7 | 3 | 5 |
| 4 | 8 | 4 | 7 |
| 4 | 9 | 5 | 2 |
| 4 | 14 | 10 | 1 |
| 5 | 6 | 1 | 22 |
| 5 | 6 | NA | 210 |
| 5 | 7 | 2 | 10 |
| 5 | 8 | 3 | 3 |
| 5 | 9 | 4 | 1 |
| 5 | 10 | 5 | 3 |
| 5 | 11 | 6 | 4 |
| 5 | 12 | 7 | 1 |
| 5 | 13 | 8 | 1 |
| 5 | 15 | 10 | 1 |
| 5 | 42 | 37 | 1 |
| 6 | 6 | 0 | 3 |
| 6 | 7 | 1 | 36 |
| 6 | 7 | NA | 258 |
| 6 | 8 | 2 | 14 |
| 6 | 9 | 3 | 9 |
| 6 | 10 | 4 | 3 |
| 6 | 11 | 5 | 8 |
| 6 | 12 | 6 | 1 |
| 6 | 13 | 7 | 1 |
| 6 | 18 | 12 | 1 |
| 6 | 24 | 18 | 1 |
| 7 | 8 | 1 | 51 |
| 7 | 8 | NA | 303 |
| 7 | 9 | 2 | 17 |
| 7 | 10 | 3 | 11 |
| 7 | 11 | 4 | 3 |
| 7 | 12 | 5 | 7 |
| 7 | 13 | 6 | 1 |
| 7 | 15 | 8 | 1 |
| 7 | 19 | 12 | 1 |
| 7 | 24 | 17 | 1 |
| 8 | 8 | 0 | 2 |
| 8 | 9 | 1 | 75 |
| 8 | 9 | NA | 378 |
| 8 | 10 | 2 | 16 |
| 8 | 11 | 3 | 17 |
| 8 | 12 | 4 | 8 |
| 8 | 13 | 5 | 1 |
| 8 | 14 | 6 | 2 |
| 8 | 15 | 7 | 1 |
| 8 | 16 | 8 | 2 |
| 8 | 20 | 12 | 1 |
| 8 | 23 | 15 | 1 |
| 9 | 9 | 0 | 2 |
| 9 | 10 | 1 | 56 |
| 9 | 10 | NA | 368 |
| 9 | 11 | 2 | 43 |
| 9 | 12 | 3 | 20 |
| 9 | 13 | 4 | 12 |
| 9 | 14 | 5 | 7 |
| 9 | 17 | 8 | 1 |
| 9 | 19 | 10 | 1 |
| 9 | 20 | 11 | 1 |
| 10 | 10 | 0 | 4 |
| 10 | 11 | 1 | 86 |
| 10 | 11 | NA | 447 |
| 10 | 12 | 2 | 48 |
| 10 | 13 | 3 | 18 |
| 10 | 14 | 4 | 8 |
| 10 | 15 | 5 | 5 |
| 10 | 16 | 6 | 2 |
| 10 | 20 | 10 | 2 |
| 10 | 35 | 25 | 1 |
| 11 | 11 | 0 | 3 |
| 11 | 12 | 1 | 128 |
| 11 | 12 | 98 | 1 |
| 11 | 12 | NA | 431 |
| 11 | 13 | 2 | 57 |
| 11 | 14 | 3 | 16 |
| 11 | 15 | 4 | 11 |
| 11 | 16 | 5 | 6 |
| 11 | 17 | 6 | 3 |
| 11 | 18 | 7 | 1 |
| 12 | 12 | 0 | 5 |
| 12 | 13 | 1 | 130 |
| 12 | 13 | NA | 436 |
| 12 | 14 | 2 | 55 |
| 12 | 15 | 3 | 38 |
| 12 | 16 | 4 | 20 |
| 12 | 17 | 5 | 9 |
| 12 | 18 | 6 | 3 |
| 12 | 19 | 7 | 1 |
| 13 | 13 | 0 | 7 |
| 13 | 14 | 1 | 136 |
| 13 | 14 | NA | 418 |
| 13 | 15 | 2 | 72 |
| 13 | 16 | 3 | 33 |
| 13 | 17 | 4 | 12 |
| 13 | 18 | 5 | 9 |
| 13 | 19 | 6 | 3 |
| 13 | 20 | 7 | 1 |
| 13 | 22 | 9 | 1 |
| 13 | 25 | 12 | 1 |
| 14 | 14 | 0 | 13 |
| 14 | 15 | 1 | 150 |
| 14 | 15 | NA | 455 |
| 14 | 16 | 2 | 75 |
| 14 | 17 | 3 | 38 |
| 14 | 18 | 4 | 16 |
| 14 | 19 | 5 | 9 |
| 14 | 20 | 6 | 2 |
| 14 | 21 | 7 | 1 |
| 14 | 22 | 8 | 1 |
| 14 | 24 | 10 | 3 |
| 15 | 15 | 0 | 8 |
| 15 | 16 | 1 | 151 |
| 15 | 16 | NA | 464 |
| 15 | 17 | 2 | 72 |
| 15 | 18 | 3 | 29 |
| 15 | 19 | 4 | 10 |
| 15 | 20 | 5 | 7 |
| 15 | 21 | 6 | 3 |
| 15 | 22 | 7 | 1 |
| 16 | 16 | 0 | 8 |
| 16 | 17 | 1 | 149 |
| 16 | 17 | 98 | 1 |
| 16 | 17 | NA | 467 |
| 16 | 18 | 2 | 83 |
| 16 | 19 | 3 | 29 |
| 16 | 20 | 4 | 13 |
| 16 | 21 | 5 | 3 |
| 16 | 22 | 6 | 3 |
| 17 | 17 | 0 | 6 |
| 17 | 18 | 1 | 149 |
| 17 | 18 | NA | 398 |
| 17 | 19 | 2 | 76 |
| 17 | 20 | 3 | 34 |
| 17 | 21 | 4 | 14 |
| 17 | 22 | 5 | 6 |
| 18 | 18 | 0 | 11 |
| 18 | 19 | 1 | 140 |
| 18 | 19 | NA | 412 |
| 18 | 20 | 2 | 79 |
| 18 | 21 | 3 | 27 |
| 18 | 22 | 4 | 12 |
| 18 | 23 | 5 | 4 |
| 18 | 24 | 6 | 1 |
| 18 | 26 | 8 | 3 |
| 18 | 27 | 9 | 1 |
| 19 | 19 | 0 | 10 |
| 19 | 20 | 1 | 133 |
| 19 | 20 | NA | 396 |
| 19 | 21 | 2 | 79 |
| 19 | 22 | 3 | 30 |
| 19 | 23 | 4 | 12 |
| 19 | 24 | 5 | 4 |
| 19 | 25 | 6 | 2 |
| 19 | 31 | 12 | 1 |
| 20 | 20 | 0 | 8 |
| 20 | 21 | 1 | 131 |
| 20 | 21 | NA | 396 |
| 20 | 22 | 2 | 67 |
| 20 | 23 | 3 | 19 |
| 20 | 24 | 4 | 8 |
| 20 | 25 | 5 | 3 |
| 20 | 26 | 6 | 2 |
| 20 | 28 | 8 | 1 |
| 21 | 21 | 0 | 6 |
| 21 | 22 | 1 | 100 |
| 21 | 22 | NA | 347 |
| 21 | 23 | 2 | 53 |
| 21 | 24 | 3 | 16 |
| 21 | 25 | 4 | 8 |
| 21 | 26 | 5 | 4 |
| 22 | 22 | 0 | 10 |
| 22 | 23 | 1 | 100 |
| 22 | 23 | NA | 295 |
| 22 | 24 | 2 | 46 |
| 22 | 25 | 3 | 27 |
| 22 | 26 | 4 | 7 |
| 22 | 27 | 5 | 2 |
| 22 | 36 | 14 | 1 |
| 23 | 23 | 0 | 7 |
| 23 | 24 | 1 | 104 |
| 23 | 24 | NA | 276 |
| 23 | 25 | 2 | 39 |
| 23 | 26 | 3 | 14 |
| 23 | 27 | 4 | 7 |
| 23 | 28 | 5 | 3 |
| 23 | 30 | 7 | 1 |
| 24 | 24 | 0 | 3 |
| 24 | 25 | 1 | 79 |
| 24 | 25 | NA | 262 |
| 24 | 26 | 2 | 43 |
| 24 | 27 | 3 | 19 |
| 24 | 28 | 4 | 2 |
| 24 | 29 | 5 | 2 |
| 24 | 31 | 7 | 1 |
| 25 | 25 | 0 | 2 |
| 25 | 26 | 1 | 64 |
| 25 | 26 | NA | 187 |
| 25 | 27 | 2 | 30 |
| 25 | 28 | 3 | 12 |
| 25 | 29 | 4 | 8 |
| 25 | 33 | 8 | 1 |
| 26 | 26 | 0 | 6 |
| 26 | 27 | 1 | 58 |
| 26 | 27 | NA | 180 |
| 26 | 28 | 2 | 30 |
| 26 | 29 | 3 | 9 |
| 26 | 30 | 4 | 3 |
| 26 | 31 | 5 | 1 |
| 27 | 27 | 0 | 3 |
| 27 | 28 | 1 | 45 |
| 27 | 28 | NA | 131 |
| 27 | 29 | 2 | 29 |
| 27 | 30 | 3 | 11 |
| 27 | 31 | 4 | 4 |
| 27 | 32 | 5 | 3 |
| 27 | 33 | 6 | 1 |
| 27 | 34 | 7 | 1 |
| 28 | 28 | 0 | 5 |
| 28 | 29 | 1 | 46 |
| 28 | 29 | NA | 100 |
| 28 | 30 | 2 | 17 |
| 28 | 31 | 3 | 10 |
| 28 | 32 | 4 | 3 |
| 28 | 33 | 5 | 1 |
| 29 | 29 | 0 | 3 |
| 29 | 30 | 1 | 32 |
| 29 | 30 | NA | 89 |
| 29 | 31 | 2 | 11 |
| 29 | 32 | 3 | 2 |
| 29 | 33 | 4 | 5 |
| 29 | 35 | 6 | 1 |
| 30 | 30 | 0 | 1 |
| 30 | 31 | 1 | 18 |
| 30 | 31 | NA | 60 |
| 30 | 32 | 2 | 11 |
| 30 | 33 | 3 | 1 |
| 31 | 31 | 0 | 4 |
| 31 | 32 | 1 | 15 |
| 31 | 32 | NA | 42 |
| 31 | 33 | 2 | 6 |
| 31 | 34 | 3 | 5 |
| 31 | 35 | 4 | 2 |
| 32 | 32 | 0 | 1 |
| 32 | 33 | 1 | 12 |
| 32 | 33 | NA | 49 |
| 32 | 34 | 2 | 8 |
| 32 | 35 | 3 | 1 |
| 33 | 33 | 0 | 1 |
| 33 | 34 | 1 | 14 |
| 33 | 34 | NA | 41 |
| 33 | 35 | 2 | 7 |
| 34 | 35 | 1 | 11 |
| 34 | 35 | NA | 26 |
| 34 | 36 | 2 | 1 |
| 34 | 37 | 3 | 2 |
| 34 | 39 | 5 | 1 |
| 35 | 35 | 0 | 1 |
| 35 | 36 | 1 | 10 |
| 35 | 36 | NA | 19 |
| 35 | 37 | 2 | 4 |
| 35 | 38 | 3 | 1 |
| 36 | 36 | 0 | 1 |
| 36 | 37 | 1 | 5 |
| 36 | 37 | NA | 11 |
| 36 | 38 | 2 | 3 |
| 37 | 37 | 0 | 1 |
| 37 | 38 | 1 | 3 |
| 37 | 38 | NA | 11 |
| 37 | 39 | 2 | 2 |
| 37 | 40 | 3 | 1 |
| 37 | 41 | 4 | 1 |
| 38 | 39 | 1 | 2 |
| 38 | 39 | NA | 7 |
| 38 | 40 | 2 | 4 |
| 38 | 42 | 4 | 1 |
| 39 | 40 | 1 | 2 |
| 39 | 40 | NA | 5 |
| 39 | 43 | 4 | 1 |
| 40 | 41 | 1 | 1 |
| 40 | 41 | NA | 2 |
| 40 | 42 | 2 | 1 |
| 41 | 42 | NA | 3 |
| 41 | 44 | 3 | 1 |
| 42 | 43 | 1 | 4 |
| 42 | 43 | NA | 5 |
| 43 | 44 | NA | 1 |
| 43 | 45 | 2 | 1 |
| 44 | 45 | NA | 1 |
| 44 | 47 | 3 | 1 |
| 45 | 46 | 1 | 1 |
| 45 | 46 | NA | 3 |
| 46 | 47 | NA | 2 |
| 47 | 48 | NA | 2 |
| 49 | 50 | NA | 1 |
| 51 | 52 | NA | 1 |
| 56 | 57 | NA | 1 |
| 86 | 87 | NA | 1 |
| 88 | 89 | NA | 2 |
| NA | NA | NA | 7359 |
| Characteristic | N = 20,9121 |
|---|---|
| Animal naming (correct - errors) | |
| 0 | 68 (0.5%) |
| 1 | 35 (0.3%) |
| 2 | 70 (0.5%) |
| 3 | 109 (0.8%) |
| 4 | 162 (1.2%) |
| 5 | 257 (1.9%) |
| 6 | 335 (2.5%) |
| 7 | 396 (2.9%) |
| 8 | 504 (3.7%) |
| 9 | 511 (3.8%) |
| 10 | 621 (4.6%) |
| 11 | 657 (4.8%) |
| 12 | 697 (5.1%) |
| 13 | 693 (5.1%) |
| 14 | 763 (5.6%) |
| 15 | 745 (5.5%) |
| 16 | 756 (5.6%) |
| 17 | 683 (5.0%) |
| 18 | 690 (5.1%) |
| 19 | 667 (4.9%) |
| 20 | 635 (4.7%) |
| 21 | 534 (3.9%) |
| 22 | 488 (3.6%) |
| 23 | 451 (3.3%) |
| 24 | 411 (3.0%) |
| 25 | 304 (2.2%) |
| 26 | 287 (2.1%) |
| 27 | 228 (1.7%) |
| 28 | 182 (1.3%) |
| 29 | 143 (1.1%) |
| 30 | 91 (0.7%) |
| 31 | 74 (0.5%) |
| 32 | 71 (0.5%) |
| 33 | 63 (0.5%) |
| 34 | 41 (0.3%) |
| 35 | 35 (0.3%) |
| 36 | 20 (0.1%) |
| 37 | 19 (0.1%) |
| 38 | 14 (0.1%) |
| 39 | 8 (<0.1%) |
| 40 | 4 (<0.1%) |
| 41 | 4 (<0.1%) |
| 42 | 9 (<0.1%) |
| 43 | 2 (<0.1%) |
| 44 | 2 (<0.1%) |
| 45 | 4 (<0.1%) |
| 46 | 2 (<0.1%) |
| 47 | 2 (<0.1%) |
| 49 | 1 (<0.1%) |
| 51 | 1 (<0.1%) |
| 56 | 1 (<0.1%) |
| 86 | 1 (<0.1%) |
| 88 | 2 (<0.1%) |
| Unknown | 7,359 |
| 1 n (%) | |
Word recall
I found two variables PD174 and PD184 that are the number of words recalled correctly, so we don’t need to recreate these scores with PD182M1-PD182M13 and PD183M1-PD183M13.
No recoding was done.
Are we going to use both immediate and delayed recall? Or just delayed recall?
| Word recall - Immediate | NUMBER GOOD - IMMEDIATE | n |
|---|---|---|
| 0 | 0 | 181 |
| 1 | 1 | 172 |
| 2 | 2 | 600 |
| 3 | 3 | 1574 |
| 4 | 4 | 3321 |
| 5 | 5 | 4712 |
| 6 | 6 | 4534 |
| 7 | 7 | 2912 |
| 8 | 8 | 1324 |
| 9 | 9 | 426 |
| 10 | 10 | 109 |
| NA | NA | 1047 |
| Characteristic | N = 20,9121 |
|---|---|
| Word recall - Immediate | |
| 0 | 181 (0.9%) |
| 1 | 172 (0.9%) |
| 2 | 600 (3.0%) |
| 3 | 1,574 (7.9%) |
| 4 | 3,321 (17%) |
| 5 | 4,712 (24%) |
| 6 | 4,534 (23%) |
| 7 | 2,912 (15%) |
| 8 | 1,324 (6.7%) |
| 9 | 426 (2.1%) |
| 10 | 109 (0.5%) |
| Unknown | 1,047 |
| 1 n (%) | |
| Word recall - Delayed | NUMBER GOOD - DELAYED | n |
|---|---|---|
| 0 | 0 | 1255 |
| 1 | 1 | 815 |
| 2 | 2 | 1517 |
| 3 | 3 | 2847 |
| 4 | 4 | 3986 |
| 5 | 5 | 4107 |
| 6 | 6 | 2942 |
| 7 | 7 | 1472 |
| 8 | 8 | 627 |
| 9 | 9 | 234 |
| 10 | 10 | 62 |
| NA | NA | 1048 |
| Characteristic | N = 20,9121 |
|---|---|
| Word recall - Delayed | |
| 0 | 1,255 (6.3%) |
| 1 | 815 (4.1%) |
| 2 | 1,517 (7.6%) |
| 3 | 2,847 (14%) |
| 4 | 3,986 (20%) |
| 5 | 4,107 (21%) |
| 6 | 2,942 (15%) |
| 7 | 1,472 (7.4%) |
| 8 | 627 (3.2%) |
| 9 | 234 (1.2%) |
| 10 | 62 (0.3%) |
| Unknown | 1,048 |
| 1 n (%) | |
Numeracy
Recoded 996, 997, and 999 as missing.
| Number series | CALCULATED NUMBER SERIES SCORE | n |
|---|---|---|
| 409 | 409 | 159 |
| 413 | 413 | 124 |
| 429 | 429 | 226 |
| 435 | 435 | 112 |
| 462 | 462 | 424 |
| 465 | 465 | 279 |
| 484 | 484 | 251 |
| 485 | 485 | 216 |
| 488 | 488 | 97 |
| 489 | 489 | 862 |
| 501 | 501 | 1132 |
| 503 | 503 | 1069 |
| 513 | 513 | 1058 |
| 514 | 514 | 830 |
| 518 | 518 | 1028 |
| 519 | 519 | 809 |
| 524 | 524 | 376 |
| 525 | 525 | 375 |
| 528 | 528 | 720 |
| 529 | 529 | 897 |
| 536 | 536 | 1254 |
| 537 | 537 | 1430 |
| 546 | 546 | 984 |
| 547 | 547 | 229 |
| 549 | 549 | 714 |
| 558 | 558 | 937 |
| 567 | 567 | 519 |
| 570 | 570 | 285 |
| 584 | 584 | 449 |
| NA | 996 | 945 |
| NA | 997 | 915 |
| NA | 999 | 258 |
| NA | NA | 949 |
| Characteristic | N = 20,9121 |
|---|---|
| Number series | |
| 409 | 159 (0.9%) |
| 413 | 124 (0.7%) |
| 429 | 226 (1.3%) |
| 435 | 112 (0.6%) |
| 462 | 424 (2.4%) |
| 465 | 279 (1.6%) |
| 484 | 251 (1.4%) |
| 485 | 216 (1.2%) |
| 488 | 97 (0.5%) |
| 489 | 862 (4.8%) |
| 501 | 1,132 (6.3%) |
| 503 | 1,069 (6.0%) |
| 513 | 1,058 (5.9%) |
| 514 | 830 (4.7%) |
| 518 | 1,028 (5.8%) |
| 519 | 809 (4.5%) |
| 524 | 376 (2.1%) |
| 525 | 375 (2.1%) |
| 528 | 720 (4.0%) |
| 529 | 897 (5.0%) |
| 536 | 1,254 (7.0%) |
| 537 | 1,430 (8.0%) |
| 546 | 984 (5.5%) |
| 547 | 229 (1.3%) |
| 549 | 714 (4.0%) |
| 558 | 937 (5.3%) |
| 567 | 519 (2.9%) |
| 570 | 285 (1.6%) |
| 584 | 449 (2.5%) |
| Unknown | 3,067 |
| 1 n (%) | |
Rescale cognitive items
The continuous cognitive items were rescaled using the min/max normalization.
| Characteristic | N = 20,912 |
|---|---|
| Animal naming (correct - errors) | |
| N Non-missing | 13,553 |
| Mean (SD) | 0.18 (0.08) |
| Median (Q1, Q3) | 0.18 (0.13, 0.24) |
| Min, Max | 0.00, 1.00 |
| Unknown | 7,359 |
| Word recall - Immediate | |
| N Non-missing | 19,865 |
| Mean (SD) | 0.53 (0.17) |
| Median (Q1, Q3) | 0.50 (0.40, 0.60) |
| Min, Max | 0.00, 1.00 |
| Unknown | 1,047 |
| Word recall - Delayed | |
| N Non-missing | 19,864 |
| Mean (SD) | 0.43 (0.20) |
| Median (Q1, Q3) | 0.40 (0.30, 0.60) |
| Min, Max | 0.00, 1.00 |
| Unknown | 1,048 |
| Number series | |
| N Non-missing | 17,845 |
| Mean (SD) | 0.64 (0.18) |
| Median (Q1, Q3) | 0.66 (0.54, 0.73) |
| Min, Max | 0.00, 1.00 |
| Unknown | 3,067 |
Model results
A single factor CFA model was fit to the items.
Model 1
The first model used all the items.
The model fit for this model was “poor”.
| Fit statistic | Value |
|---|---|
| RMSEA : Estimate | 0.087 |
| CFI | 0.903 |
| SRMR | 0.057 |
| Item | Label | Std Factor Loading |
|---|---|---|
| vdori | Orientation to time | 0.484 |
| vdlfl1z | Animal naming | 0.593 |
| vdlfl2 | Scissors & cactus | 0.661 |
| vdlfl3 | President & vice-president | 0.581 |
| vdwdimmz | Immediate word recall | 0.686 |
| vdwddelz | Delayed word recall | 0.681 |
| vdexf7z | Number series | 0.613 |
| vdsevens | Serial sevens | 0.664 |
| vdcount | Count backwards from 20 | 0.545 |
Model 2
The second model removed the immediate word recall item.
The model fit for this model was “good”.
| Fit statistic | Value |
|---|---|
| RMSEA : Estimate | 0.057 |
| CFI | 0.961 |
| SRMR | 0.041 |
| Item | Label | Std Factor Loading |
|---|---|---|
| vdori | Orientation to time | 0.485 |
| vdlfl1z | Animal naming | 0.591 |
| vdlfl2 | Scissors & cactus | 0.678 |
| vdlfl3 | President & vice-president | 0.599 |
| vdwddelz | Delayed word recall | 0.589 |
| vdexf7z | Number series | 0.642 |
| vdsevens | Serial sevens | 0.706 |
| vdcount | Count backwards from 20 | 0.567 |
Factor scores
This is the distribution of the factor scores from the final model.
Norming the factor score
The HRS sample was filtered to those that were used as the norming sample in HCAP.
The demographic variables were centered to the mean values in the norming sample. The same centering values were used in the full HRS sample.
These are the centering values used:
| variable | mean |
|---|---|
| female | 0.601 |
| black | 0.148 |
| hisp | 0.100 |
| SCHLYRS | 13.329 |
Summary of the centered demographic variables in the norming sample:
| Characteristic | N = 1,7871 |
|---|---|
| Female (centered to HCAP normal sample) | |
| -0.601007274762173 | 713 / 1,787 (40%) |
| 0.398992725237827 | 1,074 / 1,787 (60%) |
| Black (centered to HCAP normal sample) | |
| -0.148293228875209 | 1,522 / 1,787 (85%) |
| 0.851706771124791 | 265 / 1,787 (15%) |
| Hispanic (centered to HCAP normal sample) | |
| -0.0996082820369341 | 1,609 / 1,787 (90%) |
| 0.900391717963066 | 178 / 1,787 (10.0%) |
| School years (centered to HCAP normal sample) | 0.00 (2.81) |
| Unknown | 2 |
| 1 n / N (%); Mean (SD) | |
Summary of the centered demographic variables in the full HRS sample:
| Characteristic | N = 20,9121 |
|---|---|
| Female (centered to HCAP normal sample) | |
| -0.601007274762173 | 8,664 / 20,909 (41%) |
| 0.398992725237827 | 12,245 / 20,909 (59%) |
| Unknown | 3 |
| Black (centered to HCAP normal sample) | |
| -0.148293228875209 | 16,475 / 20,909 (79%) |
| 0.851706771124791 | 4,434 / 20,909 (21%) |
| Unknown | 3 |
| Hispanic (centered to HCAP normal sample) | |
| -0.0996082820369341 | 17,482 / 20,909 (84%) |
| 0.900391717963066 | 3,427 / 20,909 (16%) |
| Unknown | 3 |
| School years (centered to HCAP normal sample) | -0.6 (3.3) |
| Unknown | 91 |
| 1 n / N (%); Mean (SD) | |
Age was modeled with a cubic regression spline. The placement of the knots was determined in the norming sample, using the default percentiles of 5%, 35%, 65%, and 95%. These same knots were used in creating the splines in the full HRS sample.
Theses are the knots used:
5% 35% 65% 95%
65 70 76 85
To double check the math, these splines were compared to those from the rms::rcs() function.
These are the first 10 rows from the manually created splines.
| Age spline 1 (from HCAP normal sample) | Age spline 2 (from HCAP normal sample) | Age spline 3 (from HCAP normal sample) |
|---|---|---|
| 70 | 0.312 | 0.000 |
| 79 | 6.710 | 1.710 |
| 79 | 6.710 | 1.710 |
| 85 | 15.950 | 5.400 |
| 77 | 4.314 | 0.853 |
| 77 | 4.314 | 0.853 |
| 75 | 2.500 | 0.312 |
| 77 | 4.314 | 0.853 |
| 69 | 0.160 | 0.000 |
| 88 | 20.900 | 7.425 |
These are the first 10 rows from the rms::rcs() function.
| hrs16_cog_norm | hrs16_cog_norm' | hrs16_cog_norm'' |
|---|---|---|
| 70 | 0.313 | 0.000 |
| 79 | 6.710 | 1.710 |
| 79 | 6.710 | 1.710 |
| 85 | 15.950 | 5.400 |
| 77 | 4.314 | 0.853 |
| 77 | 4.314 | 0.853 |
| 75 | 2.500 | 0.313 |
| 77 | 4.314 | 0.853 |
| 69 | 0.160 | 0.000 |
| 88 | 20.900 | 7.425 |
Factor Score Transformation
The factor score was transformed using a Blom transformation. The transformation was done in the norming sample. Cubic regression splines of the factor score were created to predict the Blom score from the factor score. These cubic regression splines were recreated in the HRS sample to get predicted Blom scores in the HRS sample.
The knots used to create the cubic splines for the Factor score are:
5% 35% 65% 95%
-1.18125 -0.09350 0.50775 1.29150
The regression model is able to predict the blom score well using the factor score cubic splines.
The cubic splines using the same knots were recreated in the full HRS sample and were used to predict the blom transformed factor score.
Normalization of factor scores
The factor scores were normalized by regressing the predicted blom score on the demographic variables described above in the norming sample. Two way interactions between the variables were included.
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.029 | 1.182 | 0.024 | 0.981 |
| spage1 | 0.002 | 0.018 | 0.123 | 0.902 |
| spage2 | −0.138 | 0.077 | −1.792 | 0.073 |
| spage3 | 0.297 | 0.174 | 1.709 | 0.088 |
| cfemale | −3.593 | 2.358 | −1.524 | 0.128 |
| cblack | −1.828 | 3.186 | −0.574 | 0.566 |
| chisp | −4.370 | 3.896 | −1.122 | 0.262 |
| cschlyrs | 0.233 | 0.445 | 0.523 | 0.601 |
| spage1:cfemale | 0.053 | 0.035 | 1.495 | 0.135 |
| spage1:cblack | 0.015 | 0.048 | 0.307 | 0.759 |
| spage1:chisp | 0.055 | 0.058 | 0.936 | 0.349 |
| spage1:cschlyrs | −0.001 | 0.007 | −0.143 | 0.886 |
| spage2:cfemale | −0.194 | 0.155 | −1.257 | 0.209 |
| spage2:cblack | −0.024 | 0.214 | −0.113 | 0.910 |
| spage2:chisp | −0.176 | 0.280 | −0.628 | 0.530 |
| spage2:cschlyrs | 0.010 | 0.030 | 0.344 | 0.731 |
| spage3:cfemale | 0.421 | 0.348 | 1.210 | 0.226 |
| spage3:cblack | 0.023 | 0.488 | 0.048 | 0.962 |
| spage3:chisp | 0.344 | 0.650 | 0.529 | 0.597 |
| spage3:cschlyrs | −0.033 | 0.067 | −0.499 | 0.618 |
| cfemale:cblack | 0.160 | 0.116 | 1.386 | 0.166 |
| cfemale:chisp | −0.082 | 0.139 | −0.585 | 0.559 |
| cfemale:cschlyrs | 0.023 | 0.014 | 1.622 | 0.105 |
| cblack:cschlyrs | 0.010 | 0.021 | 0.472 | 0.637 |
| chisp:cschlyrs | −0.084 | 0.018 | −4.708 | 0.000 |
Plots of observed vs model implied blom scores
These are plots of the observed blom scores versus the model implied blom scores that adjusted for demographic characteristics.
The regression model was used to estimate the normalized blom scores in the full HRS sample.
Transforming to T-scores
The normalized blom scores were transformed to a T-score using the following equation:
T= 50 + 10 * ((Predicted blom score - Normalized blom score)/(sd(predicted blom score)*sqrt(1-r2)))
The sd of the predicted blom score in the norming sample is: 0.999.
The adjusted model r2 is 0.389 .
| Characteristic | N = 1,7871 |
|---|---|
| Predicted Blom score (model from HCAP normal sample) | 0.00 (1.00) |
| Unknown | 11 |
| Adjusted Blom score (from to HCAP normal sample) | 0.00 (0.63) |
| Unknown | 2 |
| T-scaled F score (from to HCAP normal sample) | 50 (10) |
| Unknown | 13 |
| 1 Mean (SD) | |
| Characteristic | N = 20,9121 |
|---|---|
| Predicted Blom score (model from HCAP normal sample) | -0.20 (1.10) |
| Unknown | 942 |
| Adjusted Blom score (from to HCAP normal sample) | -0.16 (0.82) |
| Unknown | 91 |
| T-scaled F score (from to HCAP normal sample) | 49 (12) |
| Unknown | 1,030 |
| 1 Mean (SD) | |
Designing the algorithm
This section details how we design the algorithm to mimic the Manly-Jones (2022) algorithm that we implemented in HCAP. In the previous sections we created the components that go into the algorithm - the cognitive factor score, the functional impairment measure, the subjective cognitive complaints indicator. However, these components are different than the ones used in HCAP. So, we need to find cutpoints for these components that give similar prevalence as HCAP.
These are the diagrams of the HCAP algorithm and the proposed HRS algorithm. In both algorithms the fist step is to decide if a participant has normal cognition, mild impairment, or severe impairment. After finding the cutoffs to use to determine cognitive impairment level, the next step is to find cutoffs to determine functional impairment. The functional impairment cutoffs will be determined separately for each level of cognitive impairment.
Finding cognitive impairment thresholds
Figure 12 shows the cognitive factor scores are highly correlated between the HCAP cognitive battery and the HRS cognitive battery. The correlation between them is 0.697.
Figure 13 shows the distribution of the HCAP cognitive factor score by the number of cognitive domains impaired. The plot shows there is good separation in the distribution of the HCAP factor scores by the number of domains impaired.
Figure 14 is a similar figure that shows the distribution of the HRS cognitive factor score by the number of HCAP cognitive domains impaired. This figure shows there is more overlap of the distributions of the factor scores when separating them by the same variable as in Figure 13.
Table 64 shows that 19.6% had one domain impaired, and 15.7% had two or more domains impaired. So we’ll find cutpoints in the HRS factor score to match these percentages.
| Characteristic | N = 2,9931 |
|---|---|
| Number of domains impaired | |
| No domains | 1,935 (65%) |
| 1 domain | 587 (20%) |
| 2+ domains | 471 (16%) |
| 1 n (%) | |
The values of the HRS factor score that match those percentages are:
15% 35%
36.43890 43.77599
| Characteristic | N = 2,9931 |
|---|---|
| Level of cognitive impairment (HRS) | |
| None | 1,941 (67%) |
| Mild | 572 (20%) |
| Severe | 405 (14%) |
| Unknown | 75 |
| 1 n (%) | |
Table 66 shows that the agreement of level of cognitive impairment is high between the two measures. But despite choosing thresholds of the factor score to match the percentages of the number of impaired domains, the agreement is high but not perfect. The weighted kappa is 0.49.
Level of cognitive impairment (HRS)
|
Total | ||||
|---|---|---|---|---|---|
| None | Mild | Severe | Unknown | ||
| Number of domains impaired | |||||
| No domains | 1,528 | 307 | 88 | 12 | 1,935 |
| 1 domain | 312 | 164 | 99 | 12 | 587 |
| 2+ domains | 101 | 101 | 218 | 51 | 471 |
| Total | 1,941 | 572 | 405 | 75 | 2,993 |
Finding functional impairment thresholds
The next step of the algorithm after finding cognitive impairment thresholds is to find thresholds to for functional impairment. This will be done separately by cognitive impairment level.
In the HCAP algorithm, in the 2 or more impaired domains category, 391 / (391 + 211) = 65% had functional impairments. If we use a threshold of 1 or more ADL/IADL impairments then 53.6 % will be categorized as having funcitonal impairment.
| Characteristic | N = 4051 |
|---|---|
| Sum of ADL/IADL impairments | |
| 0 | 188 (46%) |
| 1 | 97 (24%) |
| 2 | 36 (8.9%) |
| 3 | 29 (7.2%) |
| 4 | 20 (4.9%) |
| 5 | 8 (2.0%) |
| 6 | 17 (4.2%) |
| 7 | 4 (1.0%) |
| 8 | 1 (0.2%) |
| 9 | 3 (0.7%) |
| 10 | 2 (0.5%) |
| 1 n (%) | |
In the HCAP algorithm, in the 1 impaired domain category, 536 / (536 + 231) = 70% had functional impairments. If we use a threshold of 1 or more ADL/IADL impairments then 35.8 % will be categorized as having funcitonal impairment.
| Characteristic | N = 5721 |
|---|---|
| Sum of ADL/IADL impairments | |
| 0 | 367 (64%) |
| 1 | 98 (17%) |
| 2 | 54 (9.4%) |
| 3 | 20 (3.5%) |
| 4 | 16 (2.8%) |
| 5 | 8 (1.4%) |
| 6 | 6 (1.0%) |
| 7 | 3 (0.5%) |
| 1 n (%) | |
Informant based cognitive impairment
TF_missing
|
Total | ||
|---|---|---|---|
| 0 | 1 | ||
| jorm_missing | |||
| 0 | 0 | 71 | 71 |
| 1 | 2,918 | 4 | 2,922 |
| Total | 2,918 | 75 | 2,993 |
Apply algorithm in the HCAP sample
In the previous section we found the cutpoints to use to determine cognitive impairment and functional impairment levels. In this section we use those cutpoints to implement the algorithm.
The cutpoints we used for cognitive impairment were less than 36.0 and less than 43.3. The ADL/IADL cutpoints were 1 or more impairments for both of the cognitive levels. The Jorm thresholds were less than 3.0 and less than 3.4.
The following code shows a function that uses these thresholds to create the data file.
algorithm_thresholdsfunction(df){
df <- df %>%
mutate(cog_threshold = case_when(TF <36.0 ~ 2,
TF < 43.3 ~ 1,
!is.na(TF) ~ 0),
iadl_threshold = case_when(cog_threshold==2 & iadl_imp>0 ~ 1,
cog_threshold==2 & iadl_imp==0 ~ 0,
cog_threshold==1 & iadl_imp>0 ~ 1,
cog_threshold==1 & iadl_imp==0 ~ 0),
jorm_threshold = case_when(vs3jormsc>= 3.4 ~ 2,
vs3jormsc > 3.0 & vs3jormsc < 3.4 ~ 1,
vs3jormsc <= 3.0 ~ 0)
)
df
}
<bytecode: 0x128d2b050>
Version 1 of algorithm
Figure 15 shows the first version of the algorithm.
This is the code that creates a function that implements the algortihm shown in Figure 15.
Note: The algorithm uses the Jorm score from HCAP, not the HRS version.
v1_algorithmfunction(df) {
df <- df %>%
mutate(dx_v1 = case_when(
cog_threshold == 2 & iadl_threshold == 1 ~ 2,
cog_threshold == 2 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 1 ~ 2,
cog_threshold == 2 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 0 ~ 1,
cog_threshold == 1 & iadl_threshold == 1 ~ 1,
cog_threshold == 1 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 1 ~ 1,
cog_threshold == 1 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 0 ~ 0,
cog_threshold == 0 ~ 0,
is.na(TF) & jorm_threshold == 2 ~ 2,
is.na(TF) & jorm_threshold == 1 ~ 1,
is.na(TF) & jorm_threshold == 0 ~ 0
))
df <- df %>%
labelled::set_variable_labels(dx_v1 = "Algorithm (V1) Dx") %>%
labelled::set_value_labels(dx_v1 = c("Normal" = 0, "MCI" = 1, "Dementia" = 2))
df
}
<bytecode: 0x128abbe18>
| Algorithm (V1) Dx | cog_threshold | iadl_threshold | Compared to two years ago, would you say your memory is better now, about the same, or worse now than it was then? | jorm_threshold | n |
|---|---|---|---|---|---|
| 0 | 0 | NA | 0 | 0 | 927 |
| 0 | 0 | NA | 0 | 1 | 428 |
| 0 | 0 | NA | 0 | 2 | 108 |
| 0 | 0 | NA | 1 | 0 | 228 |
| 0 | 0 | NA | 1 | 1 | 171 |
| 0 | 0 | NA | 1 | 2 | 79 |
| 0 | 1 | 0 | 0 | 0 | 157 |
| 0 | 1 | 0 | 0 | 1 | 84 |
| 0 | 1 | 0 | 0 | 2 | 29 |
| 0 | NA | NA | 0 | 0 | 1 |
| 0 | NA | NA | NA | 0 | 8 |
| 1 | 1 | 0 | 1 | 0 | 34 |
| 1 | 1 | 0 | 1 | 1 | 40 |
| 1 | 1 | 0 | 1 | 2 | 22 |
| 1 | 1 | 1 | 0 | 0 | 50 |
| 1 | 1 | 1 | 0 | 1 | 36 |
| 1 | 1 | 1 | 0 | 2 | 28 |
| 1 | 1 | 1 | 1 | 0 | 27 |
| 1 | 1 | 1 | 1 | 1 | 26 |
| 1 | 1 | 1 | 1 | 2 | 38 |
| 1 | 2 | 0 | 0 | 0 | 66 |
| 1 | 2 | 0 | 0 | 1 | 32 |
| 1 | 2 | 0 | 0 | 2 | 29 |
| 1 | NA | NA | 0 | 1 | 3 |
| 1 | NA | NA | NA | 1 | 12 |
| 2 | 2 | 0 | 1 | 0 | 18 |
| 2 | 2 | 0 | 1 | 1 | 18 |
| 2 | 2 | 0 | 1 | 2 | 25 |
| 2 | 2 | 1 | 0 | 0 | 49 |
| 2 | 2 | 1 | 0 | 1 | 26 |
| 2 | 2 | 1 | 0 | 2 | 62 |
| 2 | 2 | 1 | 1 | 0 | 8 |
| 2 | 2 | 1 | 1 | 1 | 23 |
| 2 | 2 | 1 | 1 | 2 | 48 |
| 2 | 2 | 1 | NA | 2 | 1 |
| 2 | NA | NA | NA | 2 | 51 |
| NA | 1 | 0 | NA | 0 | 1 |
The following table shows the sample sizes at the places labeled in Figure 15.
| address | n |
|---|---|
| a1 | 75 |
| a2 | 2513 |
| b2 | 405 |
| b1 | 24 |
| b3 | 9 |
| c4 | 15 |
| c1 | 51 |
| b4 | 572 |
| c2 | 217 |
| c3 | 188 |
| d1 | 61 |
| d2 | 127 |
| c5 | 205 |
| c6 | 367 |
| d3 | 96 |
| d4 | 270 |
| c7 | 1941 |
| e1 | 0 |
| e2 | 329 |
| e3 | 443 |
| e4 | 2220 |
| Characteristic | N = 2,9931 |
|---|---|
| Algorithm (V1) Dx | |
| Normal | 2,220 (74%) |
| MCI | 443 (15%) |
| Dementia | 329 (11%) |
| Unknown | 1 |
| HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1) | |
| Normal (1) | 2,059 (69%) |
| MCI (2) | 652 (22%) |
| Dementia (3) | 282 (9.4%) |
| 1 n (%) | |
Algorithm (V1) Dx
|
Total | ||||
|---|---|---|---|---|---|
| Normal | MCI | Dementia | Unknown | ||
| HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1) | |||||
| Normal (1) | 1,789 | 201 | 68 | 1 | 2,059 |
| MCI (2) | 374 | 168 | 110 | 0 | 652 |
| Dementia (3) | 57 | 74 | 151 | 0 | 282 |
| Total | 2,220 | 443 | 329 | 1 | 2,993 |
$kappa
[1] 0.3390473
$weighted.kappa
[1] 0.5238401
Version 2
In version 2 of the algorithm we removed the self-rated concerns from the severe cognitive impairment portion of the algorithm. Now, if a participant has severe cognitive impairment but no functional impairment, they will be classified as MCI.
This is the code that creates a function that implements the algortihm shown in Figure 16.
v2_algorithmfunction(df) {
df <- df %>%
mutate(dx_v2 = case_when(
cog_threshold == 2 & iadl_threshold == 1 ~ 2,
cog_threshold == 2 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 1 ~ 1,
cog_threshold == 2 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 0 ~ 1,
cog_threshold == 1 & iadl_threshold == 1 ~ 1,
cog_threshold == 1 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 1 ~ 1,
cog_threshold == 1 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 0 ~ 0,
cog_threshold == 0 ~ 0,
is.na(TF) & jorm_threshold == 2 ~ 2,
is.na(TF) & jorm_threshold == 1 ~ 1,
is.na(TF) & jorm_threshold == 0 ~ 0
))
df <- df %>%
labelled::set_variable_labels(dx_v2 = "Algorithm (V2) Dx") %>%
labelled::set_value_labels(dx_v2 = c("Normal" = 0, "MCI" = 1, "Dementia" = 2))
df
}
<bytecode: 0x11bd0ace0>
| Algorithm (V2) Dx | cog_threshold | iadl_threshold | Compared to two years ago, would you say your memory is better now, about the same, or worse now than it was then? | jorm_threshold | n |
|---|---|---|---|---|---|
| 0 | 0 | NA | 0 | 0 | 927 |
| 0 | 0 | NA | 0 | 1 | 428 |
| 0 | 0 | NA | 0 | 2 | 108 |
| 0 | 0 | NA | 1 | 0 | 228 |
| 0 | 0 | NA | 1 | 1 | 171 |
| 0 | 0 | NA | 1 | 2 | 79 |
| 0 | 1 | 0 | 0 | 0 | 157 |
| 0 | 1 | 0 | 0 | 1 | 84 |
| 0 | 1 | 0 | 0 | 2 | 29 |
| 0 | NA | NA | 0 | 0 | 1 |
| 0 | NA | NA | NA | 0 | 8 |
| 1 | 1 | 0 | 1 | 0 | 34 |
| 1 | 1 | 0 | 1 | 1 | 40 |
| 1 | 1 | 0 | 1 | 2 | 22 |
| 1 | 1 | 1 | 0 | 0 | 50 |
| 1 | 1 | 1 | 0 | 1 | 36 |
| 1 | 1 | 1 | 0 | 2 | 28 |
| 1 | 1 | 1 | 1 | 0 | 27 |
| 1 | 1 | 1 | 1 | 1 | 26 |
| 1 | 1 | 1 | 1 | 2 | 38 |
| 1 | 2 | 0 | 0 | 0 | 66 |
| 1 | 2 | 0 | 0 | 1 | 32 |
| 1 | 2 | 0 | 0 | 2 | 29 |
| 1 | 2 | 0 | 1 | 0 | 18 |
| 1 | 2 | 0 | 1 | 1 | 18 |
| 1 | 2 | 0 | 1 | 2 | 25 |
| 1 | NA | NA | 0 | 1 | 3 |
| 1 | NA | NA | NA | 1 | 12 |
| 2 | 2 | 1 | 0 | 0 | 49 |
| 2 | 2 | 1 | 0 | 1 | 26 |
| 2 | 2 | 1 | 0 | 2 | 62 |
| 2 | 2 | 1 | 1 | 0 | 8 |
| 2 | 2 | 1 | 1 | 1 | 23 |
| 2 | 2 | 1 | 1 | 2 | 48 |
| 2 | 2 | 1 | NA | 2 | 1 |
| 2 | NA | NA | NA | 2 | 51 |
| NA | 1 | 0 | NA | 0 | 1 |
The following table shows the sample sizes at the places labeled in Figure 16.
| address | n |
|---|---|
| a1 | 75 |
| a2 | 2513 |
| b1 | 405 |
| b2 | 572 |
| b3 | 1941 |
| c1 | 24 |
| c2 | 9 |
| d1 | 51 |
| d2 | 217 |
| d3 | 188 |
| d4 | 15 |
| d5 | 205 |
| d6 | 367 |
| e1 | 96 |
| e2 | 270 |
| f1 | 0 |
| f2 | 268 |
| f3 | 504 |
| f4 | 2220 |
| Characteristic | N = 611 |
|---|---|
| Algorithm (V1) Dx | |
| Normal | 0 (0%) |
| MCI | 0 (0%) |
| Dementia | 61 (100%) |
| Algorithm (V2) Dx | |
| Normal | 0 (0%) |
| MCI | 61 (100%) |
| Dementia | 0 (0%) |
| HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1) | |
| Normal (1) | 15 (25%) |
| MCI (2) | 22 (36%) |
| Dementia (3) | 24 (39%) |
| 1 n (%) | |
| Characteristic | N = 2,9931 |
|---|---|
| Algorithm (V2) Dx | |
| Normal | 2,220 (74%) |
| MCI | 504 (17%) |
| Dementia | 268 (9.0%) |
| Unknown | 1 |
| HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1) | |
| Normal (1) | 2,059 (69%) |
| MCI (2) | 652 (22%) |
| Dementia (3) | 282 (9.4%) |
| 1 n (%) | |
Algorithm (V1) Dx
|
Total | ||||
|---|---|---|---|---|---|
| Normal | MCI | Dementia | Unknown | ||
| HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1) | |||||
| Normal (1) | 1,789 | 201 | 68 | 1 | 2,059 |
| MCI (2) | 374 | 168 | 110 | 0 | 652 |
| Dementia (3) | 57 | 74 | 151 | 0 | 282 |
| Total | 2,220 | 443 | 329 | 1 | 2,993 |
Algorithm (V2) Dx
|
Total | ||||
|---|---|---|---|---|---|
| Normal | MCI | Dementia | Unknown | ||
| HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1) | |||||
| Normal (1) | 1,789 | 216 | 53 | 1 | 2,059 |
| MCI (2) | 374 | 190 | 88 | 0 | 652 |
| Dementia (3) | 57 | 98 | 127 | 0 | 282 |
| Total | 2,220 | 504 | 268 | 1 | 2,993 |
$kappa
[1] 0.3337944
$weighted.kappa
[1] 0.5156637
Algorithm (V2) Dx
|
Total | ||||
|---|---|---|---|---|---|
| Normal | MCI | Dementia | Unknown | ||
| Algorithm (V1) Dx | |||||
| Normal | 2,220 | 0 | 0 | 0 | 2,220 |
| MCI | 0 | 443 | 0 | 0 | 443 |
| Dementia | 0 | 61 | 268 | 0 | 329 |
| Unknown | 0 | 0 | 0 | 1 | 1 |
| Total | 2,220 | 504 | 268 | 1 | 2,993 |